By Rich Pedott and ANJALI BURKINS

For decades talent, past sales and gut instinct drove retail assortments. Gurus who could interpret the market and build a merchandising strategy ruled fashion alongside of visionary designers. Key business inputs came down to three basic questions:

  1. Who is the customer?
  2. What are they buying?
  3. Why do they purchase?

Now, in the new era of retail, the industry is fueled by data not just by intuition. Data driven by artificial intelligence and findings driven by machine learning have become best friends to merchants and are integrated into every role within savvy organizations. Smart retailers have now pivoted to a philosophy of “facts not feelings” and are embracing a new way of working, making decisions and running their businesses. But the path of digital transformation is neither smooth nor straight. So how are retailers balancing art with science? And how is AI actually transforming business and improving the customer experience? Our recent online forum, “A Playbook on How to Choose and Use Data,” digs into digital transformation and how to master the process.

Analytics as Assets

Like anything new, change requires initial understanding and patience and, ultimately, sustained acceptance and support by all stakeholders. Introducing artificial intelligence-driven analytics to an organization has two key pillars to make it successful: people and data. Both need to be addressed, equally. People need to understand how AI can help them work, not replace them as workers. When digital transformation is initiated, people need to be trained to develop new skills and encouraged to embrace change without resorting to old methods. AI is a tool, not a silver bullet, and it should be used to augment operations, alleviating the workforce from mindless, repetitive tasks.

Talented merchants will always play a key role in a successful retailer, along with marketers and customer service teams. What has changed is that AI can actually free up time to do more of what humans are good at—relating to other humans. AI does not have sense or sensibility, so it cannot touch and feel and determine if something tastes good or feels soft (at least not yet). It can, however, create a story based on systemized data pulled from all customer sources across platforms and locations and combine them with product attributes and market information to create a story and reveal predictive opportunities.

Talented merchants will always play a key role in a successful retailer, along with marketers and customer service teams. What has changed is that AI can actually free up time to do more of what humans are good at—relating to other humans.

AI is a retailer’s friend. It can be applied at every step of the customer and product journey and applied with speed and accuracy. AI can take complicated data feeds at granular levels and process findings and KPIs that can be used to improve performance and customer experience. By predicting customer preferences, AI can help ensure that shoppers are presented with the right assortments; it can be used to create more accurate forecasts so items are in stock; it can localize assortments by location and season and identify local trends; it can allow for suggestive selling with relevant products that are personalized at the shopper level and even assist allocation and replenishment strategies to optimize selling by location at the style, color, size levels.

Is Privacy Killing Data Collection?

Retailers are now understanding that unrestricted data, customer information and selling/transactional behaviors can be used for good not evil. But has digital transformation been fettered by privacy rules and increased regulations? A little. But not really. In a cookie-less world, brands are now pivoting to mining their own proprietary data, not relying on third-party data. The emergence of loyalty programs and memberships like Prime and Walmart+ are driving data acquisition. Use of transactional data and even subscription services and third-party partnerships like Uber and Grub Hub delivery can be opportunities to gain data access to your customers with permission. Owned data is better and sidesteps the new privacy protocols. Customers can choose with whom they want to build deeper relationships and how they want brands to interact with them. Retailers are in the powerful position with first-party data to focus their efforts on engaging with more qualified, more loyal consumers. A win-win.

Who Owns AI??

In a nutshell, everyone in an organization owns AIIt does not live independently and cannot be optimized without cross-functional acceptance. A data rich strategy requires that nearly every role at every level in a company embraces and executes against it. While the information systems, data analytics and retail systems teams initiate and drive many of the components of AI and digital transformation, long-term, scalable acceptance of ML findings cascades from the C-suite down. This elevates the immediate need to educate an organization in every function about what AI is and how ML can be applied. It also requires an understanding of how it can benefit them in their roles and the profitability of the company and the satisfaction of the customer. A holistic view of an intangible, sophisticated function can be translated to everyday application.

In turn, AI requires a balance of both data scientists and human interpreters who can translate the findings and apply them to the business. The commerce + art balance reimagined. This further defines the nature of the future workforce, and the skills organizations need to find or cultivate from within their talent. New core competencies of retail organizations will include the ability to: understand data, apply/use data effectively, pivot and embrace an agile work environment and the willingness to embrace change. And if employees are to be more technologically savvy, technology will need to be more user friendly and intuitive so that they can succeed. A quid pro quo of sorts.

Mistakes? You’ll Make More than a Few

Like any transformative experience, creating an AI/ML-driven business model will not be an overnight success. One of the biggest missteps organizations make is underestimating the intimidation factor that employees may have and not proactively educating them and facing early concerns. As referenced earlier, adaptation of any key initiative requires vision, alignment, focus and prioritization. And we will add, consistency. Digital transformation must be a long-game play. Leaders need to fully understand the maturity of their organizations and the tolerance of the workforce to change before they engage them. They also need to instill change management leaders/champions and help remove obstacles and mind blocks from employees in order for them to embrace the new without feeling threatened.

Now What?

Launching a digital transformation initiative is a strategy to transform a business model. To help make it sustainable, starting with smaller progressive steps will prevent organizational fatigue and the feeling of being overwhelmed and defeated. Change requires people + process + technology to evolve and adapt in unison. Showing tangible successes and sharing the wins throughout the organization will facilitate transformation and build a stronger foundation from which to grow.

The not-so-good news is that once your organization has fully embraced the integration of AI/ML into how they work, a new technology will have emerged. The one thing we do know is only change endures, and evolution is inevitable. What exists and works today will not be around forever. Data-driven analytics is just another step into the future. That said, once an organization has shifted its mindset to accept emerging technologies and has a system in place to facilitate it, it’s on the road for systematic reinventing and transformation to respond to a marketplace in constant flux. For more on this topic, watch “A Playbook on How to Choose and Use Data” here.

* This article was republished from THE ROBIN REPORT.

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